Instructions to use jondurbin/bagel-20b-v04 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jondurbin/bagel-20b-v04 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="jondurbin/bagel-20b-v04", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jondurbin/bagel-20b-v04", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- f04ca510b18099a3890ab41fce0c9c8eecdbd854c847c57fff77239386b87dfe
- Size of remote file:
- 2.15 MB
- SHA256:
- 9d922a78a6f7d2de37f094d9eef558fd87dfc8e8df293c195aae27cb402b4160
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.